- Unlike vanilla GANs, CGAN, is a condition to both the generator and the discriminator. This condition tells the GAN what image we are expecting our generator to generate. So, both of our components—the discriminator and the generator—act upon this condition.
- The code, c, is basically interpretable disentangled information. Assuming we have some MNIST data, then, code, c1, implies the digit label, code, c2, implies the width, c3, implies the stroke of the digit, and so on. We collectively represent them by the term c.
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Mutual information between two random variables tells us the amount of information we can obtain from one random variable through another. Mutual information between two random variables x and y can be given as follows:
It is basically the difference between the entropy of y and the conditional entropy of y...
Hands-On Deep Learning Algorithms with Python
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Hands-On Deep Learning Algorithms with Python
By:
Overview of this book
Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities.
This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE.
By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.
Table of Contents (17 chapters)
Preface
Free Chapter
Section 1: Getting Started with Deep Learning
Introduction to Deep Learning
Getting to Know TensorFlow
Section 2: Fundamental Deep Learning Algorithms
Gradient Descent and Its Variants
Generating Song Lyrics Using RNN
Improvements to the RNN
Demystifying Convolutional Networks
Learning Text Representations
Section 3: Advanced Deep Learning Algorithms
Generating Images Using GANs
Learning More about GANs
Reconstructing Inputs Using Autoencoders
Exploring Few-Shot Learning Algorithms
Assessments
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